Dysferlinopathy comprises a group of autosomal recessive muscular dystrophies caused by mutations in the DYSF gene. Due to the large size of the gene and its lack of mutational hot spots, analysis of the DYSF gene is time-consuming and laborious using conventional sequencing methods. By next-generation sequencing (NGS), DYSF gene analysis has previously been validated through its incorporation in multi-gene panels or exome analyses. However, individual validation of NGS approaches for DYSF gene has not been performed. Here, we established and validated a hybridization capture-based target-enrichment followed by next-generation sequencing to detect mutations in patients with dysferlinopathy. With this approach, mean depth of coverage was approximately 450 fold and almost all (99.3%) of the targeted region had sequence coverage greater than 20 fold. When this approach was tested on samples from patients with known DYSF mutations, all known mutations were correctly retrieved. Using this method on 32 consecutive patient samples with dysferlinopathy, at least two pathogenic variants were detected in 28 (87.5%) samples and at least one pathogenic variant was identified in all samples. Our results suggested that the NGS-based screening method could facilitate efficient and accurate genetic diagnosis of dysferlinopathy.
Bibliographical noteFunding Information:
This work was supported in part by funding from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), the Ministry of Health & Welfare, Republic of Korea (Grant No: HI14C0070 to JH Lee), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning ( NRF-2012-R1A1A3007521 ), the Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2014 ( Grants No: C0217133 ), and a faculty research grant of Yonsei University College of Medicine for 2013 ( 6-2013-0149 to YC Choi).
All Science Journal Classification (ASJC) codes
- Pediatrics, Perinatology, and Child Health
- Clinical Neurology